Adrian Bulat
To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First
Bulat, Adrian; Yang, Jing; Tzimiropoulos, Georgios
Abstract
© Springer Nature Switzerland AG 2018. This paper is on image and face super-resolution. The vast majority of prior work for this problem focus on how to increase the resolution of low-resolution images which are artificially generated by simple bilinear down-sampling (or in a few cases by blurring followed by down-sampling). We show that such methods fail to produce good results when applied to real-world low-resolution, low quality images. To circumvent this problem, we propose a two-stage process which firstly trains a High-to-Low Generative Adversarial Network (GAN) to learn how to degrade and downsample high-resolution images requiring, during training, only unpaired high and low-resolution images. Once this is achieved, the output of this network is used to train a Low-to-High GAN for image super-resolution using this time paired low- and high-resolution images. Our main result is that this network can be now used to effectively increase the quality of real-world low-resolution images. We have applied the proposed pipeline for the problem of face super-resolution where we report large improvement over baselines and prior work although the proposed method is potentially applicable to other object categories.
Citation
Bulat, A., Yang, J., & Tzimiropoulos, G. (2018). To Learn Image Super-Resolution, Use a GAN to Learn How to Do Image Degradation First. In Computer Vision – ECCV 2018: 15th European Conference Munich, Germany, September 8–14, 2018 Proceedings, Part VI (187-202). https://doi.org/10.1007/978-3-030-01231-1_12
Presentation Conference Type | Edited Proceedings |
---|---|
Conference Name | 15th European Conference on Computer Vision |
Start Date | Sep 8, 2018 |
End Date | Sep 14, 2018 |
Acceptance Date | Jul 3, 2018 |
Online Publication Date | Oct 5, 2018 |
Publication Date | Oct 6, 2018 |
Deposit Date | Oct 15, 2018 |
Publicly Available Date | Oct 15, 2018 |
Publisher | Springer Publishing Company |
Volume | 11210 LNCS |
Pages | 187-202 |
Series ISSN | 1611-3349 |
Book Title | Computer Vision – ECCV 2018: 15th European Conference Munich, Germany, September 8–14, 2018 Proceedings, Part VI |
ISBN | 9783030012304 |
DOI | https://doi.org/10.1007/978-3-030-01231-1_12 |
Keywords | Image and face super-resolution; Generative Adversarial Networks; GANs |
Public URL | https://nottingham-repository.worktribe.com/output/1164422 |
Publisher URL | https://link.springer.com/chapter/10.1007/978-3-030-01231-1_12 |
Additional Information | Conference Acronym: ECCV; Conference Name: European Conference on Computer Vision; Conference City: Munich; Conference Country: Germany; Conference Year: 2018; Conference Start Date: 8 September 2018; Conference End Date: 14 September 2018; Conference Number: 15; Conference ID: eccv2018; Conference URL: https://eccv2018.org/ |
Contract Date | Oct 15, 2018 |
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